System and method for generating realistic simulation data for training an autonomous driver

Abstract

A method for training a model for generating simulation data for training an autonomous driving agent, comprising: analyzing real data, collected from a driving environment, to identify a plurality of environment classes, a plurality of moving agent classes, and a plurality of movement pattern classes; generating a training environment, according to one environment class; and in at least one training iteration: generating, by a simulation generation model, a simulated driving environment according to the training environment and according to a plurality of generated training agents, each associated with one of the plurality of agent classes and one of the plurality of movement pattern classes; collecting simulated driving data from the simulated environment; and modifying at least one model parameter of the simulation generation model to minimize a difference between a simulation statistical fingerprint, computed using the simulated driving data, and a real statistical fingerprint, computed using the real data.

Description

RELATED APPLICATIONS[0001]This application is a continuation of U.S. patent application Ser. No. 17 / 286,526 filed on Apr. 19, 2021 which claims the benefit of priority of PCT Patent Application No. PCT / IL2019 / 051119 having International Filing Date of Oct. 15, 2019, which claims the benefit of priority under 35 USC § 119(e) of U.S. Provisional Patent Application No. 62 / 746,607 filed on Oct. 17, 2018. The contents of the above applications are all incorporated by reference as if fully set forth herein in their entirety.FIELD AND BACKGROUND OF THE INVENTION[0002]The present invention, in some embodiments thereof, relates to creating a simulated model of a geographical area, and, more specifically, but not exclusively, to creating a simulated model of a geographical area, optionally including vehicle traffic to generate simulation sensory data for training an autonomous driving system.[0003]When generating simulated data for training an autonomous driving system there is a need to gene...

AI Technical Summary

Benefits of technology

The patent describes a method for training a model to generate simulation data for training autonomous driving agents. The method involves analyzing real input data to identify different environmental, moving agent, and movement pattern classes. A training environment is then generated based on one of the environmental classes and the training agents. Simulated driving data is collected from the simulated driving environment, and model parameters are modified to minimize the difference between the simulation data and the real data. The method also includes normalizing the scores used to compute the model score to increase accuracy.

Problems solved by technology

However, many images generated by a rendering engine do not appear photo-realistic.

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